Monitoring of microturning process using acoustic emission signals

Abstract

The great challenge of modern industry is to carry out an online prediction in the shop floor during the machining to define the exact tool breakage instant and simultaneously improve the quality of manufactured products. Acoustic emission sensors have been used to monitoring traditional and non-traditional machining processes. This work shows a study of the online monitoring in the microturning process using an acoustic emission sensor. A factorial design was performed to examine the effect of the feed rate, depth of cut, cooling system, and the type of tool on the response acoustic emission signal. Moreover, the acoustic emission signal was correlated with surface roughness and microhardness. The results showed that the acoustic emission signals are sensitive with the progressive increase in surface roughness and the microhardness.

Optimization of Dimensional Tolerances and Material Removal Rate in the Orthogonal Turning of AISI 4340 Steel

“…Several experiments were carried out and the data thus obtained is used for the training and testing of an artificial neural network. Ribeiro Filho et al [29] used an acoustic emission sensor for online monitoring of the micro turning process. Babu et al [30] presented the usage of copper nanofluids with minimum quantity lubrication (MQL) in turning on EN24 steel.…”